Nowadays, high-resolution satellite images and aerial photographs have become more commonly used to quickly figure out a wide devastated area in which disasters such as floods or earthquakes have occurred. Electronic data on such satellite images and aerial photographs include positional information that can be, when geometric correction is applied thereto, displayed in a manner overlaid on the existing map, whereby it becomes possible to exactly figure out and analyze the disaster situation. However, such analysis is dependent on the visual check of an expertise, which requires huge cost and time. Thus, there has been an increasing need for automation of the analysis or a method for assisting in the visual check.
As shown in FIG. 1, the cost of image data 101 on a satellite image or an aerial photograph depends on its area. Therefore, typically, an image data range 102 requested by a user is cut out and provided. Such image data range 102 often has an indefinite shape. Examples of the image data range 102 include ancient tombs.
Meanwhile, the format of a typical image file is defined such that the range of a square or a rectangle is stored. Thus, most of electronic data on satellite images and aerial photographs that are provided are not satisfied with the observed data. In many cases, the pixel value of an area that contains no image data is defined by a constant such as zero. Hereinafter, an area containing no image data will be referred to as an ignored area 103.
Among image analysis processes is a two-dimensional wavelet transformation. In the two-dimensional wavelet transformation, each of x components and y components is decomposed into high-frequency components and low-frequency components. FIG. 2 is a conceptual diagram for when a two-dimensional wavelet transformation is performed twice. High-frequency components of both x and y components that are orthogonal to the original image are indicated by HH1 (201), high-frequency components in the x direction of the original image and low-frequency components in the y direction of the original image are indicated by HL1 (202), low-frequency components in the x direction of the original image and high-frequency components in the y direction of the original image are indicated by LH1 (203), and there are also low-frequency components of both x and y components that are orthogonal the original image. Further, as a result of performing a two-dimensional wavelet transformation again on the low-frequency components of both the x and y components that are orthogonal to the original image, the low-frequency components of both the x and y components that are orthogonal to the original image are decomposed into high-frequency components of both the x and y components: HH2 (204); the high-frequency components of both the x and y components that are orthogonal to the original image are decomposed into low-frequency components in the x direction and low-frequency components in the y direction: HL2 (205); the low-frequency components of both the x and y components that are orthogonal to the original image are decomposed into low-frequency components in the x direction and high-frequency components in the y direction: LH2 (206); and the low-frequency components of both the x and y components that are orthogonal to the original image are decomposed into low-frequency components of both the x and y components: LL2 (207). Likewise, any hierarchical transformation can be performed as the two-dimensional wavelet transformation. Such characteristics of the two-dimensional wavelet transformation are used to perform statistical analysis on each scale, so as to be applied to extraction of an edge from an image, correction of the image quality, compression of image data, and the like.
Non Patent Literature 1 below is known technical literature related to the present invention.